A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Abstract

Estimation of battery state and parameters play an important role in electric vehicle battery management system (BMS). Second-order RC model is applied, the initial parameters of battery model are determined by experiments. Data points of open circuit voltage and state of charge (OCV-SOC) are determined by experiment. Different function forms are used to fit the OCV-SOC discrete points, and the function form with great fitting effect is selected as the OCV-SOC fitting form. Dual extended Kalman filter which is divided into Parameter filter and state filter is applied. Battery state in state filter is a fast-time-varying parameter, The battery model parameters in parameter filter are divided into two parts. the battery model parameters are classified according to the influence of each parameter on the terminal voltage. A longer sampling time is applied to the parameters that have a strong impact on the terminal voltage, and a longest sampling time is applied to the parameters that have a weak impact on the terminal voltage. The time-scale classification method is validated both quantitatively and qualitatively. Compared with the previous methods, the three-time-scale classification method can reduce the number of parameter updates.